Association between underweight and hospitalization, emergency room visits, and mortality among patients in community medical homes

Trends of underweight, overweight, and obesity among older adults in China from 2008 to 2018: a national observational survey

OBJECTIVE

This study aims to investigate the 10-year trends and disparities in underweight, overweight, and obesity among older adults aged 65 years and older in China from 2008 to 2018.

METHODS

We used four waves (2008, 2011, 2014, and 2018) of data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a national community-based cross-sectional survey conducted every 2-3 years. Body weight and height were measured by trained assessors following standardized procedures. BMI was calculated and divided into underweight (< 18.5 kg/m²), normal (18.5-24.9 kg/m²), overweight (25.0-29.9 kg/m²), obese (≥ 30.0 kg/m²) according to WHO reference. Multinomial logistic regression models were used to examine factors related with abnormal BMI groups, after adjusting for potential confounders.

RESULTS

Among 46,543 older adults in China, the prevalence rates of underweight decreased with each survey year from 2008 to 2018, declining from 20.05 to 7.87% (p < 0.001). In contrast, the prevalence rates of overweight and obesity showed an increasing trend (all p < 0.001). Specifically, the prevalence of overweight rose from 12.82% to 2008 to 28.45% in 2018, and the prevalence of obesity increased from 1.62% to 2008 to 4.95% in 2018. In the multinomial logistic regression model, survey year, gender, residence, marital status, economic status, numbers of chronic diseases, smoking status, sleep quality, and functional disability were factors related with obesity.

CONCLUSION

The prevalence rates of overweight and obesity were increasing while the prevalence of underweight and normal weight significantly decreased from 2008 to 2018 among older adults in China, which poses a huge challenge for chronic disease. There is an urgent need for intervention policy planning and early prevention of abnormal body weight for the preparation of an aging society.

Hospitalizations in older-adults newly diagnosed with dementia: A population-based longitudinal study in Israel

OBJECTIVES

To compare inpatient burden (i.e. likelihood of hospitalization, number of admissions and length of stay) in persons with newly diagnosed dementia to the general population without dementia. Additionally, to evaluate whether inpatient burden is increased during the years prior to and post dementia diagnosis, and to identify factors associated with increased inpatient burden.

METHOD

The Israeli National Dementia Dataset (2016) was cross-linked with the National Hospital Discharge Database of the Israeli Ministry of Health (2014-2018). Dementia definition was based on documented dementia diagnoses and/or the purchase of medications during 2016. Mixed-effects models were applied to identify demographic and health characteristics associated with inpatient burden in the one and 2 years prior to and after dementia diagnosis.

RESULTS

The dataset included 11,625 individuals aged ≥65 years, identified as incident dementia cases. Compared to the general population of older-adults without dementia, those with newly diagnosed dementia had a higher age-standardized proportion of hospitalizations (26.4% vs. 40%). The odds for hospitalization were highest during the year preceding dementia diagnosis (OR = 3.19, 95% CI 2.51-4.06) compared to 2 years prior to diagnosis, and remained high (although slightly decreased) after dementia diagnosis. Older age was associated with inpatient burden after, but not prior to dementia diagnosis.

CONCLUSIONS

Older persons with dementia are a vulnerable population group with increased utilization of inpatient burden compared to those without dementia, particularly in the years surrounding dementia diagnosis. Sociodemographic risk factors may differ with respect to the time surrounding dementia diagnosis.

Impact of nutrition status and body mass index on mortality in hospitalized general medical patients: A prospective observational study

BACKGROUND

Malnutrition and low body mass index (BMI) are risk factors for mortality in hospitalized patients. Data substantiating this are unavailable for hospitalized general medical patients in our setting. We studied the prevalence of malnutrition among patients admitted to general medical wards in a tertiary care hospital and its role as a risk factor for 1-month mortality. We also investigated the association of BMI with mortality.

METHODS

In this prospective observational study, nutrition assessment using Subjective Global Assessment (SGA) and anthropometric measurements was performed in 395 hospitalized general medical patients. Charlson Comorbidity Index (CCI) and Modified Early Warning System (MEWS) score were calculated. Clinical course and vital status at 1 month after discharge was noted. Factors associated with mortality were identified using logistic regression.

RESULTS

The mean age of the study population was 46.2 + 16.1 years; 247 (62.5%) were males. Of 395 patients, 129 (32.7%) belonged to SGA A, 155 (39.2%) to SGA B, and 111 (28.1%) to SGA C. Mean (±SD) BMI was 23.38 (±5.33); 141 (35.6%) were obese. Mortality was observed in 61 (15.4%) patients. Patients in the lowest BMI quartile had the lowest mortality. The adjusted regression analysis showed that higher age and MEWS scores were independently associated with mortality. Severe malnourishment (SGA C) was another important predictor. Further, the odds of death increased consistently across the consecutive BMI quartiles.

CONCLUSION

Higher age, higher MEWS scores, severe malnourishment, and higher BMI scores were independent risk factors for 1-month mortality in hospitalized general medical patients.

The effects of smoking, regular drinking, and unhealthy weight on health care utilization in China

Background

Preventive risk factors such as smoking, drinking, and unhealthy weight have contributed to the accelerated rise in noncommunicable chronic diseases, which are dominant drivers of health care utilization and spending in China. However, few studies have been conducted using a large longitudinal dataset to explore the impact of such preventive risk factors on health care utilization. Therefore, this study aimed to ascertain the effects of smoking, regular drinking, and unhealthy weight on health care utilization in China.

Methods

This research was a longitudinal study using data from five waves of the China Family Panel Studies (CFPS) conducted between 2010 and 2018, and the final sample consisted of 63,260 observations (12,652 participants) across all five waves of data collection. Health care utilization was measured from two perspectives: outpatient utilization and inpatient utilization. Smoking status was categorized as never smoker, former smoker, or current smoker. Unhealthy weight was classified based on the participants’ body mass index. A fixed effects logistic regression model was used for the analysis.

Results

The results of fixed effects logistic regression showed that current and former smokers were approximately 1.9 times and 2.0 times more likely to use outpatient care than those who never smoked, respectively (odds ratio (OR) = 1.88, p < 0.05; OR = 2.03, p < 0.05). Obese people were approximately 1.3 times more likely to use outpatient care than healthy weight people (OR = 1.26, p < 0.05). Moreover, the results show that compared to those who never smoked, for current and former smokers, the odds of being hospitalized increased by 42.2 and 198.2%, respectively (OR = 1.42; p < 0.1, OR = 2.98; p < 0.05). Compared to healthy weight people, overweight and obese people were also more likely to be hospitalized (OR = 1.11; p < 0.1, OR = 1.18; p < 0.1, respectively).

Conclusion

Among Chinese adults, current and former smokers were more likely to use outpatient and inpatient care than those who had never smoked. Moreover, compared to healthy weight people, obese people were more likely to use outpatient and inpatient care, and overweight people were more likely to use inpatient care. These results may have important implications that support the government in making health care resource allocation decisions.

Body mass index and all-cause mortality in older adults: A scoping review of observational studies

In older age, body composition changes as fat mass increases and redistributes. Therefore, the current body mass index (BMI) classification may not accurately reflect risk in older adults (65+). This study aimed to review the evidence on the association between BMI and all-cause mortality in older adults and specifically, the findings regarding overweight and obese BMI. A systematic search of the OVID MEDLINE and Embase databases was conducted between 2013 and September 2018. Observational studies examining the association between BMI and all-cause mortality within a community-dwelling population aged 65+ were included. Seventy-one articles were included. Studies operationalized BMI categorically (n = 60), continuously (n = 8) or as a numerical change/group transition (n = 7). Reduced risk of mortality was observed for the overweight BMI class compared with the normal BMI class (hazard ratios [HR] ranged 0.41-0.96) and for class 1 or 2 obesity in some studies. Among studies examining BMI change, increases in BMI demonstrated lower mortality risks compared with decreases in BMI (HR: 0.83-0.95). Overweight BMI classification or a higher BMI value may be protective with regard to all-cause mortality, relative to normal BMI, in older adults. These findings demonstrate the potential need for age-specific BMI cut-points in older adults.

Weight as a Risk Factor for Mortality in Critically Ill Patients

OBJECTIVES

To explore the hypothesis that obesity is associated with increased mortality and worse outcomes in children who are critically ill.

METHODS

Secondary analysis of the Assessment of Worldwide Acute Kidney Injury, Renal Angina, and Epidemiology study, a prospective, multinational observational study. Patients between 3 months and 25 years across Asia, Australia, Europe, and North America were recruited for 3 consecutive months. Patients were divided into 4 groups (underweight, normal weight, overweight, and obese) on the basis of their BMI percentile for age and sex.

RESULTS

A total of 3719 patients were evaluated, of whom 542 (14%) had a primary diagnosis of sepsis. One thousand fifty-nine patients (29%) were underweight, 1649 (44%) were normal weight, 423 (11%) were overweight, and 588 (16%) were obese. The 28-day mortality rate was 3.6% for the overall cohort and 9.1% for the sepsis subcohort and differed significantly by weight status (5.8%, 3.1%, 2.2%, and 1.8% for subjects with underweight, normal weight, overweight, and obesity, respectively, in the overall cohort [P < .001] and 15.4%, 6.6%, 3.6%, and 4.7% in the sepsis subcohort, respectively [P = .003]). In a fully adjusted model, 28-day mortality risk was 1.8-fold higher in the underweight group versus the normal weight group in the overall cohort and 2.9-fold higher in the sepsis subcohort. Patients who were overweight and obese did not demonstrate increased risk in their respective cohorts. Patients who were underweight had a longer ICU length of stay, increased need for mechanical ventilation support, and a higher frequency of fluid overload.

CONCLUSIONS

Patients who are underweight make up a significant proportion of all patients in the PICU, have a higher short-term mortality rate, and have a more complicated ICU course.

A gradient-boosted model analysis of the impact of body mass index on the short-term outcomes of critically ill medical patients

Objective

To evaluate the impact of body mass index on the short-term prognosis of non-surgical critically ill patients while controlling for performance status and comorbidities.

Methods

We performed a retrospective analysis on a two-year single-center database including 1943 patients. We evaluated the impact of body mass index on hospital mortality using a gradient-boosted model that also included comorbidities and was assessed by Charlson’s comorbidity index, performance status and illness severity, which was measured by the SAPS3 score. The SAPS3 score was adjusted to avoid including the same variable twice in the model. We also assessed the impact of body mass index on the length of stay in the hospital after intensive care unit admission using multiple linear regressions.

Results

A low value (< 20kg/m²) was associated with a sharp increase in hospital mortality. Mortality tended to subsequently decrease as body mass index increased, but the impact of a high body mass index in defining mortality was low. Mortality increased as the burden of comorbidities increased and as the performance status decreased. Body mass index interacted with the impact of SAPS3 on patient outcome, but there was no significant interaction between body mass index, performance status and comorbidities. There was no apparent association between body mass index and the length of stay at the hospital after intensive care unit admission.

Conclusion

Body mass index does appear to influence the shortterm outcomes of critically ill medical patients, who are generally underweight. This association was independent of comorbidities and performance status.

Undernutrition, risk of malnutrition and obesity in gastroenterological patients: A multicenter study

AIM: To investigate the prevalence of undernutrition, risk of malnutrition and obesity in the Italian gastroenterological population.

METHODS: The Italian Hospital Gastroenterology Association conducted an observational, cross-sectional multicenter study. Weight, weight loss, and body mass index were evaluated. Undernutrition was defined as unintentional weight loss > 10% in the last three-six months. Values of Malnutrition Universal Screening Tool (MUST) > 2, NRS-2002 > 3, and Mini Nutritional Assessment (MNA) from 17 to 25 identified risk of malnutrition in outpatients, inpatients and elderly patients, respectively. A body mass index ≥ 30 indicated obesity. Gastrointestinal pathologies were categorized into acute, chronic and neoplastic diseases.

RESULTS: A total of 513 patients participated in the study. The prevalence of undernutrition was 4.6% in outpatients and 19.6% in inpatients. Moreover, undernutrition was present in 4.3% of the gastrointestinal patients with chronic disease, 11.0% of those with acute disease, and 17.6% of those with cancer. The risk of malnutrition increased progressively and significantly in chronic, acute and neoplastic gastrointestinal diseases in inpatients and the elderly population. Logistical regression analysis confirmed that cancer was a risk factor for undernutrition (OR = 2.7; 95%CI: 1.2-6.44, P = 0.02). Obesity and overweight were more frequent in outpatients.

CONCLUSION: More than 63% of outpatients and 80% of inpatients in gastroenterological centers suffered from significant changes in body composition and required specific nutritional competence and treatment.

Body Mass Index Trajectories and Healthcare Utilization in Young and Middle-aged Adults

The obesity epidemic is a significant public health issue with adverse impact on health and costs. Applying a life-course perspective to obesity may advance our understanding of the influence of obesity over time on patterns of healthcare utilization in young and middle-aged United States (US) adults.We identified baseline body mass index (BMI) and BMI trajectories, and assessed their association with outpatient visits, emergency department (ED) visits, and hospitalizations in a well-defined population of young and middle-aged US adults.Using the Rochester Epidemiology Project resources, we conducted a retrospective cohort study of adults (N = 23,254) aged 18 to 44 years, with at least 3 BMI measurements, residing in Olmsted County, MN from January 1, 2005 through December 31, 2012.We observed that 27.5% of the population was obese. Four BMI trajectories were identified. Compared to under/normal weight, obese class III adults had higher risk of outpatient visits (adjusted rate ratio [RR], 1.86; 95% confidence intervals [CIs], 1.67-2,08), ED visits (adjusted RR, 3.02; 95% CI, 2.74-3.34), and hospitalizations (adjusted RR, 1.67; 95% CI, 1.59-1.75). BMI trajectory was positively associated with ED visits after adjustment for age, sex, race, and Charlson Comorbidity Index (P < 0.001 for trend).Among young and middle-aged US adults, baseline BMI is positively associated with outpatient visits, ED visits, and hospitalizations, while BMI trajectory is positively associated with ED visits. These findings extend our understanding of the longitudinal influence of obesity on healthcare utilization in early to mid-adulthood.

Health behaviors and quality of life predictors for risk of hospitalization in an electronic health record-linked biobank

Background

Hospital risk stratification models using electronic health records (EHRs) often use age and comorbid health burden. Our primary aim was to determine if quality of life or health behaviors captured in an EHR-linked biobank can predict future risk of hospitalization.

Methods

Participants in the Mayo Clinic Biobank completed self-administered questionnaires at enrollment that included quality of life and health behaviors. Participants enrolled as of December 31, 2010 were followed for one year to ascertain hospitalization. Data on comorbidities and hospitalization were derived from the Mayo Clinic EHR. Hazard ratios (HR) and 95% confidence interval (CI) were used, adjusted for age and sex. We used gradient boosting machines models to integrate multiple factors. Different models were compared using C-statistic.

Results

Of the 8,927 eligible Mayo Clinic Biobank participants, 834 (9.3%) were hospitalized. Self-perceived health status and alcohol use had the strongest associations with risk of hospitalization. Compared to participants with excellent self-perceived health, those reporting poor/fair health had higher risk of hospitalization (HR =3.66, 95% CI 2.74–4.88). Alcohol use was inversely associated with hospitalization (HR =0.57 95% CI 0.45–0.72). The gradient boosting machines model estimated self-perceived health as the most influential factor (relative influence =16%). The predictive ability of the model based on comorbidities was slightly higher than the one based on the self-perceived health (C-statistic =0.67 vs 0.65).

Conclusion

This study demonstrates that self-perceived health may be an important piece of information to add to the EHR. It may be another method to determine hospitalization risk.